Path planning method and system for road maintenance vehicle

Abstract

The invention discloses a path planning method and system for a road maintenance vehicle, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-modal data of the road maintenance vehicle through a sensor and a 5g network, uploading the multi-modal data to a cloud data center, and carrying out the multi-modal data fusion; based on the fused multi-modal data, quickly calculating an optimal path set by using a quantum optimization algorithm; executing the maintenance task; for the road maintenance vehicle executing the task on the road, the cloud data center continuously receives feedback data; after the task is completed, the cloud data center gathers the operation data of the road maintenance vehicle and carries out deep analysis. The multi-modal sensor and the 5G technology are integrated, the environment is monitored in real time, the optimal path is calculated and dynamically adjusted through quantum optimization and the CADPOA algorithm, tasks are automatically executed, path planning is continuously optimized through a feedback mechanism, continuous and efficient maintenance work is achieved, and continuity and high quality of the maintenance work are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a path planning method and system for a highway maintenance vehicle. Background Art

[0002] The intelligent transformation of highway maintenance is a core issue in modern traffic management. In recent years, with the rapid development of IoT technology and 5G communication, multimodal data collection and analysis have become possible, providing a data basis for the efficient operation of highway maintenance vehicles. The low latency and high bandwidth characteristics of 5G networks ensure the reliability of real-time data transmission. The application of multimodal sensors, such as high-definition cameras, Li DAR, GPS, I MU and various environmental monitoring sensors, comprehensively captures multiple information such as road conditions, traffic flow, and vehicle status, laying a solid foundation for refined management. The advancement of data fusion technology enables data from different sensors to be effectively integrated to form a unified situational awareness, further promoting the improvement of intelligent decision-making systems.

[0003] However, although the existing technology has made significant progress in data collection and transmission, the limitations of path planning algorithms still restrict the overall improvement of highway maintenance efficiency. Traditional path planning algorithms, such as Dijkstra and AI algorithms, perform well in static environments, but are difficult to cope with complex dynamic road conditions and changing maintenance needs. They often ignore the multimodal characteristics of data, resulting in inaccurate path planning and inability to fully utilize real-time information to adjust the optimal path. In addition, the lack of an efficient real-time path adjustment mechanism makes maintenance vehicles slow to respond when faced with emergencies, affecting the continuity and safety of operations. Therefore, there is an urgent need for a new path planning method that can quickly process multimodal data, has real-time optimization capabilities and is highly adaptable, so as to significantly improve the intelligence level and operation efficiency of highway maintenance. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a path planning method and system for a highway maintenance vehicle to solve the data fusion, dynamic optimization and intelligent decision-making problems in the path planning of a highway maintenance vehicle.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the first aspect, an embodiment of the present invention provides a path planning method for a highway maintenance vehicle, which includes collecting multimodal data of the highway maintenance vehicle through sensors and 5G networks and uploading it to a cloud data center to perform multimodal data fusion; based on the fused multimodal data, using a quantum optimization algorithm to quickly calculate an optimal path set; based on the calculated optimal path, using a CADPOA algorithm to perform real-time path prediction and adjustment and uploading it to a cloud data center using a 5G network; the highway maintenance vehicle plans its own driving route and performs maintenance tasks according to the optimal path instructions issued by the cloud data center; for highway maintenance vehicles performing tasks on the road, the cloud data center will continue to receive feedback data; after completing the task, the cloud data center will summarize the highway maintenance vehicle operation data and conduct in-depth analysis.

[0008] As a preferred solution of the path planning method for a highway maintenance vehicle of the present invention, wherein: the multimodal data of the highway maintenance vehicle is collected through sensors and 5G network and uploaded to the cloud data center for multimodal data fusion, and the specific steps are as follows:

[0009] Highway maintenance vehicles collect multimodal data in real time through multimodal sensors;

[0010] Preprocess the multimodal data;

[0011] Upload the pre-processed multimodal data to the cloud data center in real time through the 5G network;

[0012] The EBFFA algorithm is used to fuse the preprocessed data sets.

[0013] As a preferred solution of the path planning method for a highway maintenance vehicle of the present invention, wherein: based on the fused multimodal data, the optimal path set is quickly calculated using a quantum optimization algorithm, and the specific steps are:

[0014] Based on the fused multimodal data, the QEMOOA algorithm is used to calculate the optimal path at time t. As a preferred solution of the path planning method for highway maintenance vehicles described in the present invention, the CADPOA algorithm is used to perform real-time path prediction and adjustment based on the calculated optimal path and upload it to the cloud data center using the 5G network. The specific steps are as follows:

[0015] Based on the optimal path, the CADPOA algorithm is used for real-time path adjustment and prediction;

[0016] The adjusted and predicted real-time path is uploaded to the cloud data center via the 5G network.

[0017] As a preferred solution of the path planning method for a highway maintenance vehicle of the present invention, the highway maintenance vehicle plans its own driving route and performs maintenance tasks according to the optimal path instruction issued by the cloud data center. The specific steps are as follows:

[0018] After receiving the optimal route instruction from the cloud data center, the road maintenance vehicle first parses the instruction, including the details of the maintenance task that the maintenance vehicle needs to perform, the expected driving route, the maintenance points along the way, the expected arrival time, and safety tips and warning information;

[0019] Based on the parsed instructions, the onboard navigation system of the highway maintenance vehicle will automatically plan the driving route;

[0020] and generates a detailed driving plan including departure time, estimated arrival time, turning points, deceleration zones, acceleration zones, and dwell time at maintenance points;

[0021] The maintenance vehicle performs maintenance tasks according to the planned route, and the on-board software monitors the real-time status of the vehicle.

[0022] As a preferred solution of the path planning method for a highway maintenance vehicle of the present invention, wherein: for a highway maintenance vehicle performing tasks on the road, the cloud data center will continuously receive feedback data, and the specific steps are as follows:

[0023] The cloud data center analyzes data uploaded by highway maintenance vehicles performing tasks on the road in real time;

[0024] Dynamically adjust path planning and evaluate its performance, including travel time, fuel efficiency, safety indicators, path deviation, and task completion rate;

[0025] When the actual travel time exceeds the expected time by 10%, path replanning is triggered;

[0026] Adjust speed when fuel efficiency is less than 12 kilometers per liter of gasoline;

[0027] When safety indicators deteriorate, preventive measures should be taken.

[0028] As a preferred solution of the path planning method for a highway maintenance vehicle of the present invention, wherein: after completing the task, the cloud data center will summarize the operation data of the highway maintenance vehicle and conduct in-depth analysis, the specific steps are:

[0029] After the highway maintenance vehicle completes its mission, the cloud data center aggregates the operation data of all highway maintenance vehicles, including vehicle status, operation efficiency, environmental factors, road condition information, operation path, safety indicators, and mission completion rate;

[0030] Preprocess the aggregated data;

[0031] Build an analysis framework to conduct in-depth analysis on different performance indicators;

[0032] The time series model is used to identify the efficiency in different time periods, and the average efficiency between different job types is tested through variance analysis;

[0033] Calculate benefits per unit cost and compare the cost-effectiveness of different operation types and vehicles;

[0034] Use geographic information GIS tools to compare the geographic coordinates of the actual and planned routes, and analyze traffic conditions and weather changes by cross-referencing real-time traffic data;

[0035] Use decision tree models to predict the best path under future road conditions, and use moving averages to identify trends in safety indicators over time;

[0036] Statistical tests were used to identify unusual safety events, and finally, Kaplan-Meier survival curves were used to estimate the duration of maintenance effect.

[0037] In the second aspect, the present invention provides a path planning system for a highway maintenance vehicle, including a data acquisition module, a fusion module, a path calculation module, a prediction and adjustment module, an execution feedback module, and an analysis and optimization module; the data acquisition module is used to collect multimodal data of the highway maintenance vehicle through sensors and 5g networks and upload it to a cloud data center for multimodal data fusion; the fusion module is used to quickly calculate an optimal path set based on the fused multimodal data using a quantum optimization algorithm; the path calculation module is used to perform real-time path prediction and adjustment using a CADPOA algorithm based on the calculated optimal path and upload it to the cloud data center using a 5g network; the prediction and adjustment module is used for the highway maintenance vehicle to plan its own driving route and perform maintenance tasks according to the optimal path instructions issued by the cloud data center; the execution feedback module is used for the cloud data center to continuously receive feedback data for highway maintenance vehicles performing tasks on the road; the analysis and optimization module is used for the cloud data center to summarize the highway maintenance vehicle operation data and conduct in-depth analysis after completing the task.

[0038] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the path planning method for a highway maintenance vehicle as described in the first aspect of the present invention is implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the path planning method for a highway maintenance vehicle as described in the first aspect of the present invention is implemented.

[0040] The beneficial effects of the present invention are as follows: the present invention realizes all-round real-time monitoring of the environment around the maintenance vehicle by integrating multimodal sensors and 5G networks, provides detailed data for path planning, and utilizes quantum optimization algorithms. This method can quickly calculate the optimal path set under multi-objective constraints, significantly reducing the driving time and fuel consumption of maintenance operations while improving safety. With the help of the CADPOA algorithm, path planning has the ability of real-time adjustment and prediction, ensuring that the maintenance vehicle can still maintain efficient operation in the face of emergencies. The maintenance vehicle automatically performs maintenance tasks according to the optimal path instructions issued by the cloud data center, thereby improving the standardization and safety of operations. The entire system continuously receives feedback data and dynamically adjusts path planning to ensure the continuity and high quality of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 This is a flow chart of the path planning method for a highway maintenance vehicle in Example 1.

[0043] Figure 2 This is a flow chart of the automated operation in Example 1. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a path planning method for a highway maintenance vehicle, comprising the following steps:

[0046] S1. Collect multimodal data of highway maintenance vehicles through sensors and 5G networks and upload them to the cloud data center for multimodal data fusion.

[0047] Furthermore, highway maintenance vehicles collect multimodal data in real time through multimodal sensors;

[0048] Multimodal data includes:

[0049] High-definition camera data: provides visual information for identifying road conditions (such as cracks, potholes), obstacles (such as fallen rocks, animals), signs and markings, and surrounding environment (such as weather and lighting conditions).

[0050] LiDAR data: measures distance and location information for building high-precision 3D maps, detecting static and dynamic obstacles, and evaluating road surface smoothness;

[0051] Ultrasonic sensor data: used for close-range obstacle detection, especially in low-visibility conditions, providing additional safety.

[0052] Thermal imaging camera data: At night or in low light conditions, it can detect heat sources such as engine overheating, heat from tire wear, and possible biological organisms;

[0053] GPS / GNSS data: provides precise location information for locating vehicles and recording driving trajectories;

[0054] Inertial measurement unit (IMU) data: includes accelerometers and gyroscopes, which are used to measure the acceleration, tilt angle, and rotation of the vehicle to assist in positioning and attitude estimation;

[0055] Traffic flow monitoring data: assessing traffic density and predicting potential congestion by detecting the number, speed and type of passing vehicles;

[0056] Meteorological sensor data: including temperature, humidity, wind speed, and rainfall, used to assess the impact of weather conditions on operations;

[0057] Vehicle status data: including engine speed, oil pressure, coolant temperature, fuel level, battery voltage, etc., used to monitor vehicle health;

[0058] Communication data: including communication information with other vehicles or infrastructure for collaborative operation and collision avoidance;

[0059] Preprocess the multimodal data;

[0060] The data is initially cleaned and formatted to remove noise and redundancy to ensure data quality. Specific preprocessing includes:

[0061] Remove duplicates, fill in missing values, correct erroneous data, and handle outliers to improve data quality;

[0062] Convert non-numeric data encoding to numerical data to improve data distribution;

[0063] Scaling the data to the same scale through minimum-maximum normalization or Z-score normalization to eliminate the dimension effect;

[0064] Identify the subset of features most relevant to the target and remove irrelevant or redundant features to reduce model complexity and improve model performance. Feature selection methods include:

[0065] Filtering methods: Use chi-square test, variance threshold and correlation coefficient to evaluate the correlation between features and target variables;

[0066] Wrapper methods: evaluate the performance of different feature subsets on a specific model through recursive feature elimination (RFE) and genetic algorithms;

[0067] Embedded methods: feature selection during model training through regularization techniques such as LASSO and Ridge regression, and decision tree algorithms such as random forest and GBDT;

[0068] Data padding strategies should be considered to cope with possible sensor failures or data loss;

[0069] Preferably, common encoding methods include:

[0070] One-hot encoding: For categorical variables, convert them into a binary vector in which only one bit represents 1 and the rest are 0, indicating the category of the categorical variable;

[0071] Label encoding: converting categorical variables into integers, with one integer corresponding to each category, but this method may introduce unnecessary order relationships;

[0072] Ordinal encoding: Similar to label encoding, but for ordered categorical variables, where the size of the number reflects the order of the categories;

[0073] Embedding coding: Using the embedding layer of a neural network to map categorical variables into a continuous vector space, this encoding method can capture the potential relationship between categories;

[0074] Upload the pre-processed multimodal data to the cloud data center in real time via the 5G network;

[0075] The EBFFA algorithm is used to fuse the preprocessed data set, and the expression is:

[0076]

[0077] Among them, F(D(t)) is the output value of the fusion algorithm, which represents the result of fusion of all data D at time t, and H i (D i (t)) is the i-th sensor data D iThe information entropy value of (t) is used to measure the uncertainty and information content of the data, F i (D i (t)) is the i-th sensor data D i The fuzzy membership function value of (t) reflects the relevance and credibility of the data and the fusion target, i is the index of the sensor data, and n is the total number of sensor data.

[0078] Preferably, H i (D i (t)) is the i-th sensor data D i The information entropy value of (t) is a statistical measure of data uncertainty or information volume. For a discrete random variable X, the information entropy is defined as H(X), and the expression is:

[0079] H(X)=-∑ x∈X P(x)log2 P(x);

[0080] Among them, H(X) represents the information entropy of random variable X, that is, the uncertainty measure of random variable X. The higher the information entropy, the greater the uncertainty of the random variable, that is, the more information it contains. x∈X represents the sum of all possible values ​​of x, where x is a value of the random variable X. For example, if X is the result of a dice throw, then the value range of x is {1, 2, 3, 4, 5, 6}, and P(x) represents the probability that the random variable X takes the value x. This probability must satisfy 0≤P(x)≤1, and the sum of the probabilities for all x∈X is equal to 1. For example, if X represents the result of a fair coin toss, then P heads = P tails = 0.5 P heads = P tails = 0.5, log2 P(x) represents the logarithm with base 2. 2 is used here because information is usually measured in bits. The role of the logarithmic function here is to convert probability into a measure of the amount of information. Because when the probability P(x) is close to 1, the event is almost certain to occur, and the amount of information is small; and when P(x) is close to 0, the event is almost certain not to occur, and the amount of information is large. The logarithm with base 2 ensures that the unit of the amount of information is bits. - is that there is a negative sign in the formula for information entropy. This is because the range of the logarithmic function is from negative infinity to zero. After adding the negative sign, the range of the information entropy becomes zero to positive infinity, so that the information entropy becomes a positive value, indicating the amount of information.

[0081] For sensor data D i (t), first count each possible state and its frequency of occurrence, and then substitute it into the above formula to calculate the information entropy. If the data is continuous, it usually needs to be discretized first;

[0082] Preferably, F i (D i(t)) is the i-th sensor data D i (t) The fuzzy membership function value, which is used to evaluate the relevance and credibility of the data and the fusion target;

[0083] The calculation of the fuzzy membership function depends on the specific application scenario and data type. Usually, it is calculated based on the similarity of the data to a known standard or pattern, such as through a distance metric or a similarity metric. For a particular sensor data, the membership can be determined by comparing the difference between its reading and the ideal or typical reading.

[0084] It should be noted that multimodal sensors are used to collect and upload the environmental and vehicle status data of highway maintenance vehicles to the cloud in real time. After preprocessing and fusion, a unified data view is formed. This process improves data quality, ensures the accuracy of decision-making, supports efficient road condition analysis and vehicle health management, realizes intelligent route planning and preventive maintenance, and ultimately optimizes the efficiency and safety of maintenance operations.

[0085] S2. Based on the fused multimodal data, the quantum optimization algorithm is used to quickly calculate the optimal path set.

[0086] Furthermore, based on the fused multimodal data, the QEMOOA algorithm is used to calculate the optimal path at time t, which is expressed as:

[0087]

[0088] The objective function is expressed as:

[0089] g = 3;

[0090] Among them, P * is the optimal path selection value, is to find the path value P, Q that maximizes the expression in the brackets g (P, t) is the quantum expectation value of the g-th objective function under path P and time t, which reflects the optimization ability of quantum computing in probability space. g is the quantum weight factor of the g-th objective function, reflecting the importance of the objective function. g is the index of the objective function, ranging from 1 to G. G is the total number of objective functions. 3 is the three special values ​​among the g objective functions, corresponding to the total travel time value T(P), the total fuel consumption value E(P) and the total safety value S(P).

[0091] Preferably, objective function definition: multiple objective functions are established, covering key indicators such as minimizing travel time, minimizing fuel consumption, and maximizing safety. These objective functions can be expressed in mathematical form as follows:

[0092] Minimize travel time: minT(P) = ∑ e∈Pt e ;

[0093] Minimize fuel consumption: minE(P) = ∑ e∈P e e ;

[0094] Maximize safety: maxS(P) = ∑ e∈P s e ;

[0095] Where T(P) represents the total travel time on path P, which is one of the objective functions to be minimized in path planning. The goal is to find the path with the least time consumption. e refers to an edge or road segment in path P. In the context of a highway maintenance vehicle, each edge can represent a road segment between two checkpoints or locations. P represents a path value, which consists of a series of connected edges and represents the route of the highway maintenance vehicle from the starting point to the end point. t e It refers to the travel time of edge e, that is, the time required for the highway maintenance vehicle to pass through the road section e. This time can be affected by many factors, such as road conditions, traffic flow, speed limit, etc. E(P) represents the total fuel consumption on path P. This is another objective function to be minimized. The purpose is to find the path with the least fuel consumption. e It refers to the energy consumption or fuel consumption of edge e. This value may depend on factors such as the length of the road section, road conditions (such as slope, road surface type), vehicle speed and load, etc. S(P) represents the overall safety value of path P. This is an objective function to be maximized in order to find the safest path. e It refers to the safety score of edge e, which can be evaluated based on multiple factors, such as accident rate, sight condition, road maintenance condition, traffic density, etc. The ∑ symbol represents the sum of all edges in path P.

[0096] Preferably, γ g This parameter is often called the quantum weight factor, which reflects the importance of each objective function; in a multi-objective optimization problem, each objective may have a different priority, γ g It is used to quantify this priority. In practice, γ g The value of can be determined by expert knowledge, empirical rules or through sensitivity analysis. It can also be an adjustable parameter, allowing the user to adjust the relative importance of the optimization objective according to actual conditions.

[0097] γ g It can be set based on the opinions of domain experts, can be adjusted based on previous solutions to similar problems, and can be adjusted by changing γ g Observe the changes in the results and find the most sensitive parameter. g As an additional optimization variable, it can be adjusted by genetic algorithms to obtain the optimal or near-optimal solution;

[0098] Q g The expression (P, t) refers to the quantum expectation value of the g-th objective function under path P and time t. The concept of quantum expectation value originates from the expectation value theory in quantum mechanics and is used to evaluate the average performance of the objective function in probability space. In multi-objective optimization, this means that for each possible path and a given time point, we can calculate the expected contribution of the path to the objective function. The calculation of this value usually involves the principles of quantum computing, such as quantum state superposition and quantum entanglement, to simulate and optimize the possible solution space;

[0099] Q g (P, t) is obtained by measuring the expected value of the wave function. In quantum circuits, the quantum expectation value of the target function is usually achieved by constructing a quantum circuit that can prepare a quantum state related to the target function. Then, the expected value of this quantum state is estimated through quantum measurement, which usually involves quantum gate operations such as Hadamard gates, control gates, and rotating gates, as well as superposition and entanglement of quantum states.

[0100] Preferably, the quantum optimization algorithm uses the properties of quantum computing, such as superposition and entanglement, to search for the optimal solution. In quantum computing, quantum bits can be in multiple states at the same time, which enables quantum computers to process multiple computing paths at the same time, thereby theoretically finding the global optimal solution faster than classical computers;

[0101] QEMOOA is a quantum-inspired multi-objective optimization algorithm that combines the principles of quantum computing and the ideas of evolutionary algorithms. It explores the solution space through the superposition and entanglement of quantum bits and uses quantum gate operations (such as Hadamard gates) to update the solution set, thereby finding a set of Pareto optimal solutions.

[0102] It should be noted that based on the fused multimodal data, the QEMOOA algorithm takes advantage of quantum computing, adjusts the target priority through quantum weight factors, evaluates the multi-objective optimization of the path with quantum expectation values, and quickly finds the optimal path set. This method combines quantum superposition and entanglement, breaks through the limitations of traditional computing, and greatly improves the efficiency and quality of path planning. It is especially suitable for complex scenarios that need to consider multi-dimensional goals such as time, cost and safety, and realizes the intelligence and optimization of highway maintenance operations.

[0103] S3. Based on the calculation of the optimal path, the CADPOA algorithm is used to perform real-time path prediction and adjustment and upload it to the cloud data center using the 5G network.

[0104] Furthermore, based on the optimal path, the CADPOA algorithm is used to perform real-time path adjustment and prediction to cope with emergencies or unforeseen changes. The expression is:

[0105]

[0106] Among them, P n (t) is the new n-path value after adjustment at time t, It is to find the specific path value that achieves the minimum value among all possible paths, which means to find the path P(t) that minimizes the expression in the brackets among all possible paths P(t), α t is the weight factor for the travel time varying with time t, β t is the weight factor of fuel consumption over time t, γ t is the weight factor of maintenance efficiency that varies with time t. T(P, t) represents the total travel time of path P at time t, which is usually determined by the speed of the maintenance vehicle, the length of the path, and the traffic conditions that may be encountered. E(P, t) represents the total fuel consumption value of path P at time t, which may depend on the fuel efficiency, load, and road conditions of the vehicle. C(P, t) represents the maintenance efficiency value of path P at time t, that is, the speed and quality of completing the maintenance task, which may be affected by the type of maintenance vehicle, the nature of the maintenance task, and the suitability of the maintenance vehicle for a specific task. δ is a constant weight factor used to balance the certainty of the path and the adaptability to unknown events. V(P, t) represents the uncertainty value of path P at time t, which takes into account the possibility of future changes in road conditions, such as traffic jams, weather changes, or sudden maintenance needs. The uncertainty V can be a probability distribution predicted based on historical data and real-time information.

[0107] Upload the adjusted and predicted real-time path to the cloud data center via the 5G network;

[0108] Preferably, α t represents the weight factor of the travel time changing with time t. Its value can be adjusted dynamically according to the urgency of the maintenance vehicle’s current task, changes in traffic conditions, or the maintenance vehicle’s schedule. For example, if the maintenance vehicle needs to complete the task as soon as possible, then α t The value of may be increased to prioritize reducing travel time; during peak traffic hours, α t May be adjusted higher to avoid longer journey times due to traffic congestion;

[0109] β t is the weight factor of fuel consumption over time t, reflecting the importance of fuel consumption at time t, which may change with fluctuations in fuel prices, the amount of remaining fuel in the vehicle, or environmental policy requirements. For example, when fuel costs rise, β t may be set higher to encourage maintenance vehicles to choose more energy-efficient routes. On long missions, when fuel reserves are low, β t It may also be adjusted higher to avoid the need to refuel en route;

[0110] γ t is the weight factor of maintenance efficiency that changes with time t. This parameter can be adjusted according to the maintenance task type, task priority, and maintenance capability of the maintenance vehicle. For example, when the maintenance vehicle performs a maintenance task with a high criticality, γ t On the other hand, if the maintenance vehicle has limited maintenance resources, then γ t May be lowered to avoid excessive consumption of resources;

[0111] δ is a constant weight factor used to balance the certainty of the path and adaptability to unknown events. This parameter usually does not change over time, but it can be set according to the risk preference of the maintenance vehicle operator or the uncertainty requirements of the task. If the operator prefers a conservative strategy to avoid potential accidents, the value of δ may be set higher. On the contrary, if the task environment is relatively stable and predictable, the value of δ may be lower.

[0112] These parameters are determined by the following steps:

[0113] Initial setting: According to the task type, environmental conditions and operator requirements of the maintenance vehicle, α t , β t , γ t and δ set reasonable initial values;

[0114] Real-time adjustment: By monitoring the real-time status of the maintenance vehicle and the external environment, such as traffic conditions and weather conditions, the values ​​of these parameters are dynamically adjusted to optimize the performance of the maintenance vehicle;

[0115] Feedback loop: fine-tune parameters based on the execution results and performance indicators of the maintenance vehicle to achieve better optimization results;

[0116] Long-term learning: Over time, through the accumulation of historical data and machine learning algorithms, the system can learn more effective parameter adjustment strategies to adapt to changing operating environments and task requirements;

[0117] Preferably, V(P,t) represents the uncertainty value of path P at time t, which reflects the uncertain factors that the path may encounter in the future, such as traffic conditions, weather changes, etc.;

[0118] V(P,t) can be estimated through statistical analysis of historical data and real-time information. For example, the possibility of traffic congestion can be predicted based on historical traffic flow data, or the impact of severe weather can be predicted using weather forecast data. In addition, probabilistic models, such as Bayesian networks or Markov chains, can be used to model and predict future uncertainties.

[0119] Preferably, the data uploading process involves steps such as data collection, preprocessing, encrypted transmission and storage, and the steps are as follows:

[0120] Collect data from sensors;

[0121] Then the data is preprocessed by cleaning, format conversion, and feature extraction;

[0122] Use SSL / TLS protocol to encrypt data and upload it through 5G network;

[0123] The data is decrypted and stored in the cloud data center;

[0124] The data security measures are as follows:

[0125] Use TLS / SSL encryption protocol;

[0126] Use algorithms such as CRC, MD5 or SHA to verify data integrity;

[0127] Use mechanisms such as OAuth and JWT to ensure data access security;

[0128] Deploy RAID systems and back up data regularly;

[0129] Use firewalls, IPS / IDS systems to prevent unauthorized access;

[0130] Specifically, during the transmission of data from the 5G network to the cloud data center, security and integrity are mainly guaranteed in the following ways:

[0131] Encrypted transmission: Use SSL / TLS protocol or other encryption methods to encrypt data to prevent data from being eavesdropped or tampered with during transmission;

[0132] Data integrity check: Use hash algorithms such as CRC, MD5 or SHA to verify whether the data remains unchanged during transmission;

[0133] Authentication and authorization: Ensure that only authorized devices and users can access data, using authentication mechanisms such as OAuth and JWT;

[0134] Redundancy and backup: Deploy RAID redundant storage systems in the data center and back up data regularly to prevent data loss;

[0135] It should be noted that the CADPOA algorithm adjusts the optimal path in real time to respond to sudden changes, and instantly uploads the adjusted path to the cloud through the 5G network. The algorithm dynamically optimizes travel time, fuel consumption and maintenance efficiency, balances certainty and uncertainty, ensures efficient and flexible highway maintenance operations, and intelligently adjusts parameters according to task urgency and environmental conditions. It combines historical and real-time data to predict future road conditions, improves decision-making accuracy, encrypts data transmission, ensures information security, supports intelligent operation of maintenance vehicles, and improves road maintenance efficiency and safety.

[0136] S4. Highway maintenance vehicles plan their own routes and perform maintenance tasks based on the optimal path instructions issued by the cloud data center.

[0137] Furthermore, after receiving the optimal route instruction from the cloud data center, the highway maintenance vehicle first parses the instruction, including the details of the maintenance task that the maintenance vehicle needs to perform, the expected driving route, the maintenance demand points along the way, the expected arrival time, and safety tips and warning information;

[0138] Based on the parsed instructions, the onboard navigation system of the highway maintenance vehicle will automatically plan the driving route;

[0139] In-car navigation takes into account the current vehicle position, the destination, traffic conditions on the projected route, weather forecasts, and possible dynamic obstacles;

[0140] and generates a detailed driving plan including departure time, estimated arrival time, turning points, deceleration zones, acceleration zones, and dwell time at maintenance points;

[0141] The maintenance vehicle performs maintenance tasks according to the planned route, and the on-board software monitors the real-time status of the vehicle.

[0142] Real-time status includes speed, fuel consumption, battery power, tire pressure, etc., to ensure that the vehicle is running in the best condition. At the same time, the system continuously monitors the vehicle's surroundings, such as detecting obstacles through cameras and radars to ensure driving safety;

[0143] It should be noted that highway maintenance vehicles independently plan routes and efficiently perform maintenance based on the optimal path instructions in the cloud. They generate accurate driving plans by intelligently analyzing task details and taking into account real-time traffic and weather to ensure that they arrive at maintenance points on time. The vehicles monitor their own status and environment throughout the process to ensure operational safety and efficiency.

[0144] S5. For highway maintenance vehicles performing tasks on the road, the cloud data center will continue to receive feedback data.

[0145] Furthermore, the cloud data center analyzes data uploaded by highway maintenance vehicles performing tasks on the road in real time;

[0146] Dynamically adjust route planning and evaluate its performance, including travel time, fuel efficiency, safety indicators, route deviation, and task completion rate, to ensure that maintenance vehicles can respond quickly to new situations, reduce invalid travel, and improve work efficiency;

[0147] Travel time: measures the deviation of actual travel time from the estimated time;

[0148] Fuel efficiency: calculates fuel consumption per unit distance traveled;

[0149] Safety indicators: evaluate the frequency of safety-related events, such as the number of emergency brakes;

[0150] Path deviation: compare the difference between the actual driving path and the planned path;

[0151] Task completion rate: measures the ratio of the number of tasks completed within the specified time to the number of planned tasks;

[0152] When the actual travel time exceeds the expected time by 10%, rerouting is triggered, which indicates that the maintenance vehicle's travel time is longer than planned, which may be caused by traffic congestion, poor road conditions or other unforeseen factors. In this case, rerouting is triggered to find a new and possibly faster route to ensure that the maintenance vehicle can complete the task in time;

[0153] When the fuel efficiency is less than 12 kilometers per liter of gasoline, adjust the speed or route to save fuel. This means that the fuel consumption of the maintenance vehicle is higher than expected. It may be due to excessive vehicle load, bad driving habits or technical problems of the vehicle itself. Adjusting the speed can help improve fuel efficiency and reduce fuel consumption, which is very important for long-term operating cost control;

[0154] Safety indicators deteriorate, and preventive measures are taken, such as slowing down or choosing a safer alternative route;

[0155] It should be noted that highway maintenance vehicles autonomously plan routes based on the optimal path instructions in the cloud and perform maintenance efficiently. The on-board system monitors the real-time status to ensure safe operation. The cloud continuously analyzes feedback data, dynamically optimizes the route, evaluates the journey, fuel, safety and task completion performance, and automatically adjusts in the event of anomalies, greatly improving operational efficiency and response speed, and realizing intelligent maintenance operations.

[0156] S6. After completing the task, the cloud data center will summarize the highway maintenance vehicle operation data and conduct in-depth analysis.

[0157] Furthermore, after the highway maintenance vehicle completes its mission, the cloud data center aggregates the operational data of all highway maintenance vehicles, including vehicle status, operational efficiency, environmental factors, road condition information, operational path, safety indicators, and mission completion rate;

[0158] Vehicle status: fuel consumption, speed, engine running status, battery charge (for electric vehicles);

[0159] Operation efficiency: the time to complete each maintenance operation, the amount of materials used, and the type of maintenance operation (e.g., repairing potholes, clearing roadblocks);

[0160] Environmental factors: weather conditions, temperature, humidity, and light intensity during operation;

[0161] Road condition information: road type (highway, urban road, etc.), road condition (flatness, degree of wear), traffic flow;

[0162] Operation path: comparison between actual driving path and planned path, and record of reasons for path changes;

[0163] Safety indicators: number of emergency brakes, number of collision warnings, and minimum safe distance from other vehicles;

[0164] Task completion rate: comparison of actual completion time and scheduled time for each task, and analysis of reasons for unfinished tasks;

[0165] Preprocess the aggregated data;

[0166] Preprocessing includes data cleaning, deduplication, outlier detection and processing, missing value filling, etc. to ensure that the analyzed data is accurate;

[0167] Build an analysis framework to conduct in-depth analysis on different performance indicators;

[0168] Use time series models to identify efficiency in different time periods, cluster similar time periods together, and identify common trends in efficiency;

[0169] Use variance analysis to test whether there are significant differences in the average efficiency between different types of operations, establish a mathematical model between operation type and efficiency, and predict the efficiency of different types of operations;

[0170] Calculate the benefits per unit cost, compare the cost effectiveness of different operation types and vehicles, explore the relationship between cost and efficiency, and identify cost saving opportunities;

[0171] Use geographic information GIS tools to compare the geographic coordinates of the actual and planned routes and identify deviations;

[0172] By cross-referencing real-time traffic data, analyze the impact of traffic conditions, weather changes, etc. on route selection;

[0173] Use decision tree models to predict the best path under future road conditions, and learn the strategy of selecting the best path under specific conditions by simulating the results of different path selections;

[0174] Use moving averages to identify trends in security indicators over time;

[0175] Identify unusual security events through statistical testing;

[0176] Kaplan-Meier survival curves were used to estimate the duration of maintenance effect and identify the time point when maintenance effect declined;

[0177] The Cox proportional hazards model was used to explore the factors affecting the durability of maintenance effects;

[0178] Conduct sentiment analysis on user feedback text to identify positive and negative feedback and understand the impact of maintenance work on road users;

[0179] Predict the probability of vehicle failure using ARI MA and state-space models;

[0180] Predict the life of vehicle components based on historical maintenance records;

[0181] Use seasonally adjusted time series models to forecast future maintenance needs;

[0182] Preferably, a time series model is a statistical model used to analyze data points arranged in chronological order, which usually represent the change of a variable over time. Time series models can identify trends, seasonal patterns, cyclical fluctuations, and other time-related behaviors in data. In the context of highway maintenance vehicles, time series models can be used to identify the operating efficiency in different time periods (such as morning peak, evening peak, weekends), help plan operating time, and predict future workload and resource requirements;

[0183] ANOVA is a statistical method used to compare whether the mean values ​​of two or more groups are significantly different. In maintenance operations, ANOVA can be used to test whether the average operation time or cost between different types of operations (such as pavement repair and sign refreshing) is significantly different, helping to identify more efficient or cost-effective operation types;

[0184] GIS tools are software systems used to collect, store, analyze and display geographic spatial data. In highway maintenance, GIS can be used to draw and compare the actual driving path of maintenance vehicles with the planned path, analyze the reasons for path deviation, and combine real-time road conditions and weather data to evaluate the effectiveness of path selection and support more reasonable path planning;

[0185] Decision trees are a type of supervised learning algorithm used for classification and regression tasks. In maintenance operations, decision tree models can predict the optimal path under future road conditions based on historical data. By considering multiple factors (such as weather, traffic flow, and construction areas), decision trees learn how to choose the best path under different conditions.

[0186] Moving average is a statistical method used to analyze time series data. By calculating the average of a series of data points, data can be smoothed and trends can be identified. In safety indicator analysis, moving average can help identify the changing trend of safety events (such as the number of emergency brakes) over time, and assist in monitoring and early warning of safety performance.

[0187] Statistical tests are tools used to determine whether the differences between sample data are statistically significant, such as t-tests and chi-square tests. In maintenance operations, statistical tests can be used to identify abnormal safety events, such as an abnormal increase in the number of emergency brakes within a certain period of time, indicating possible safety hazards.

[0188] Survival curves, especially the Kaplan-Meier curve, are used to describe the distribution of survival time of individuals or entities and are often used in medical research to evaluate treatment effects. In maintenance operations, survival curves can be used to estimate the duration of maintenance effects, identify the long-term effects of maintenance measures, and guide future maintenance strategies.

[0189] Risk models, such as the Cox proportional hazard model, are used to analyze multiple variables that affect survival time. In the field of highway maintenance, risk models can explore multiple factors that affect the durability of maintenance effects, such as material quality, construction technology, environmental conditions, etc., to help optimize maintenance plans and resource allocation.

[0190] The ARI MA model is a popular time series forecasting method, which consists of three parts: autoregression, differencing, and moving average. The autoregression model uses past observations as predictors of current values; the differencing is to make the time series stationary, and the original data needs to be differentiated, that is, the previous observation is subtracted from each observation; the moving average model uses past prediction errors to correct the prediction; the ARI MA model is usually expressed as ARI MA (p, d, q), where p is the number of autoregressive terms, d is the number of differencing, and q is the number of moving average terms; the ARI MA model is suitable for time series data without obvious seasonal patterns.

[0191] The state-space model is a mathematical framework widely used in signal processing, control system theory, and time series analysis. It treats the internal state of the system as implicit variables that cannot be directly observed, and infers these states through a set of observable outputs; it is divided into two parts: the state equation and the observation equation. The state equation describes how the system state changes over time, while the observation equation describes how the observed data depends on the system state; the state-space model is very flexible and can handle complex dynamic systems, including nonlinear and non-stationary processes, and is often used in econometrics, engineering control and other fields;

[0192] Seasonally adjusted time series models are specially designed to handle data containing cyclical patterns. Such models attempt to separate seasonal components from data in order to more accurately analyze trends and cycles. Common seasonal adjustment methods include multiplicative models and additive models. The multiplicative model decomposes the time series into trend, seasonal, and random components, assuming that the seasonal effect is proportional to the trend level. The additive model assumes that the seasonal effect is constant and does not change with the trend level. For example, based on the ARI MA model, it can be expanded to a seasonal ARI MA model, which not only contains the components of the ARI MA, but also adds seasonal autoregression and seasonal moving average parts to better capture seasonal patterns.

[0193] It should be noted that after completing the task, the cloud data center deeply analyzes the maintenance vehicle operation data, covering multiple dimensions such as vehicle status, efficiency, environment, and safety, and ensures data quality through preprocessing; uses statistical and predictive models to evaluate operation efficiency, predict cost-effectiveness, optimize route selection, monitor safety trends, evaluate maintenance persistence, and understand social impacts through user feedback, predict vehicle health and future needs, and comprehensively improve the scientificity and foresight of road maintenance decisions. The purpose is to achieve optimal resource allocation, improve maintenance efficiency, ensure road safety, and enhance public satisfaction.

[0194] This embodiment also provides a path planning system for a highway maintenance vehicle, including: a data acquisition module, a fusion module, a path calculation module, a prediction and adjustment module, an execution feedback module, and an analysis and optimization module; the data acquisition module is used to collect multimodal data of the highway maintenance vehicle through sensors and 5g networks and upload it to the cloud data center for multimodal data fusion; the fusion module is used to quickly calculate the optimal path set based on the fused multimodal data using a quantum optimization algorithm; the path calculation module is used to perform real-time path prediction and adjustment using the CADPOA algorithm on the basis of calculating the optimal path and upload it to the cloud data center using the 5g network; the prediction and adjustment module is used for the highway maintenance vehicle to plan the driving route and perform maintenance tasks according to the optimal path instructions issued by the cloud data center; the execution feedback module is used for the cloud data center to continuously receive feedback data for the highway maintenance vehicle performing tasks on the road; the analysis and optimization module is used for the cloud data center to summarize the highway maintenance vehicle operation data and conduct in-depth analysis after completing the task.

[0195] This embodiment also provides a computer device, which is suitable for the path planning method for a highway maintenance vehicle, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the path planning method for a highway maintenance vehicle as proposed in the above embodiment.

[0196] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0197] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the path planning method for a highway maintenance vehicle proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0198] In summary, the present invention solves the problems of data fusion, dynamic optimization and intelligent decision-making in the path planning of highway maintenance vehicles by integrating advanced data acquisition, quantum optimization algorithm and real-time path adjustment technology.

[0199] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a path planning method for a highway maintenance vehicle is provided.

[0200] The experiment selected two highway maintenance vehicles equipped with similar hardware configurations, marked as the innovative system vehicle and the traditional system vehicle respectively. The experiment was conducted on a typical mixed traffic highway, which includes urban sections, suburban sections and mountainous sections, with a total length of about 200 kilometers.

[0201] First, both vehicles are equipped with the same multimodal sensors, including high-definition cameras, LiDAR, ultrasonic sensors, thermal imaging cameras, GPS / GNSS modules, IMU, traffic flow monitoring equipment, meteorological sensors, vehicle status monitors and communication modules. The two vehicles set off at the same time with the goal of completing maintenance work in the designated area, including clearing roadblocks, repairing cracks and potholes, checking road signs and markings, and ensuring safe driving.

[0202] Next, the innovative system car adopted advanced data collection and quantum optimization algorithms, while the traditional system car used traditional data processing and path planning algorithms. The data collection frequency of the two cars was set to once per second to ensure the real-time and accuracy of the data.

[0203] Finally, the sensor data of the two vehicles are uploaded to the cloud data center in real time for data fusion and path planning. The innovative system vehicle uses the EBFFA algorithm and QEMOOA algorithm for data fusion and path planning, while the traditional system vehicle uses a rule-based method for data processing and path planning.

[0204] The details are shown in Table 1 below:

[0205] Table 1 Experimental record table

[0206]

[0207] From the above data, it can be seen that the innovative system vehicle using the content of the present invention is significantly better than the traditional system vehicle using the traditional path planning method in terms of travel time, fuel consumption, safety score and task completion rate. Specifically:

[0208] Travel time: The innovative system vehicle took only 120 minutes to complete the maintenance task, while the traditional system vehicle took 150 minutes, which means that the innovative system vehicle's travel time was reduced by 20%.

[0209] Fuel consumption: The fuel consumption of the innovative system car is 15.5 liters, while the fuel consumption of the traditional system car is 20.2 liters, and the fuel saving reaches 23%.

[0210] Safety score: The safety score of the innovative system vehicle is 95, which is much higher than the 88 points of the traditional system vehicle, indicating that the innovative system vehicle can better avoid risks and provide higher safety guarantees when performing tasks.

[0211] Task completion rate: The task completion rate of the innovative system vehicle reached 98%, while that of the traditional system vehicle was only 90%. This shows that the innovative system vehicle is more efficient in performing maintenance tasks and can complete the established maintenance work more effectively.

[0212] These data fully demonstrate the innovation and advantages of the content of this invention, especially in improving the operating efficiency of highway maintenance vehicles, reducing operating costs and enhancing operating safety. By adopting advanced data collection and quantum optimization algorithms, the intelligent highway maintenance vehicle path planning system can achieve better path planning, thereby significantly improving the overall efficiency of maintenance operations.

[0213] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A path planning method for a highway maintenance vehicle, characterized in that: include, Collect multimodal data from road maintenance vehicles through sensors and 5G networks and upload them to the cloud data center for multimodal data fusion; Based on the fused multimodal data, the optimal path set is quickly calculated using quantum optimization algorithms; Based on the calculation of the optimal path, the CADPOA algorithm is used to make real-time path prediction and adjustment and then uploaded to the cloud data center using the 5G network; Highway maintenance vehicles plan their own routes and perform maintenance tasks based on the optimal path instructions issued by the cloud data center; For highway maintenance vehicles performing tasks on the road, the cloud data center will continuously receive feedback data; After completing the task, the cloud data center will summarize the highway maintenance vehicle operation data and conduct in-depth analysis.

2. The path planning method for a highway maintenance vehicle according to claim 1, characterized in that: The multimodal data of the highway maintenance vehicle is collected through sensors and 5G network and uploaded to the cloud data center for multimodal data fusion. The specific steps are as follows: Highway maintenance vehicles collect multimodal data in real time through multimodal sensors; Preprocess the multimodal data; Upload the pre-processed multimodal data to the cloud data center in real time through the 5G network; The EBFFA algorithm is used to fuse the preprocessed data sets.

3. The path planning method for a highway maintenance vehicle according to claim 2, characterized in that: Based on the fused multimodal data, the optimal path set is quickly calculated using the quantum optimization algorithm. The specific steps are as follows: Based on the fused multimodal data, the QEMOOA algorithm is used to calculate the optimal path at time t.

4. The path planning method for a highway maintenance vehicle according to claim 3, characterized in that: As described above, based on the calculation of the optimal path, the CADPOA algorithm is used to perform real-time path prediction and adjustment and upload it to the cloud data center using the 5G network. The specific steps are as follows: Based on the optimal path, the CADPOA algorithm is used for real-time path adjustment and prediction; The adjusted and predicted real-time path is uploaded to the cloud data center via the 5G network.

5. The path planning method for a highway maintenance vehicle according to claim 4, characterized in that: As described above, the highway maintenance vehicle plans its own route and performs maintenance tasks according to the optimal path instructions issued by the cloud data center. The specific steps are as follows: After receiving the optimal route instruction from the cloud data center, the road maintenance vehicle first parses the instruction, including the details of the maintenance task that the maintenance vehicle needs to perform, the expected driving route, the maintenance points along the way, the expected arrival time, and safety tips and warning information; Based on the parsed instructions, the onboard navigation system of the highway maintenance vehicle will automatically plan the driving route; and generates a detailed driving plan including departure time, estimated arrival time, turning points, deceleration zones, acceleration zones, and dwell time at maintenance points; The maintenance vehicle performs maintenance tasks according to the planned route, and the on-board software monitors the real-time status of the vehicle.

6. The path planning method for a highway maintenance vehicle according to claim 5, characterized in that: As mentioned above, for the road maintenance vehicles performing tasks on the road, the cloud data center will continue to receive feedback data. The specific steps are as follows: The cloud data center analyzes data uploaded by highway maintenance vehicles performing tasks on the road in real time; Dynamically adjust path planning and evaluate its performance, including travel time, fuel efficiency, safety indicators, path deviation, and task completion rate; When the actual travel time exceeds the expected time by 10%, path replanning is triggered; Adjust speed when fuel efficiency is less than 12 kilometers per liter of gasoline; When safety indicators deteriorate, preventive measures should be taken.

7. The path planning method for a highway maintenance vehicle according to claim 6, characterized in that: As mentioned above, after completing the task, the cloud data center will summarize the highway maintenance vehicle operation data and conduct in-depth analysis. The specific steps are as follows: After the highway maintenance vehicle completes its mission, the cloud data center aggregates the operation data of all highway maintenance vehicles, including vehicle status, operation efficiency, environmental factors, road condition information, operation path, safety indicators, and mission completion rate; Preprocess the aggregated data; Build an analysis framework to conduct in-depth analysis on different performance indicators; The time series model is used to identify the efficiency in different time periods, and the average efficiency between different job types is tested through variance analysis; Calculate benefits per unit cost and compare the cost-effectiveness of different operation types and vehicles; Use geographic information GIS tools to compare the geographic coordinates of the actual and planned routes, and analyze traffic conditions and weather changes by cross-referencing real-time traffic data; Use decision tree models to predict the best path under future road conditions and use moving averages to identify trends in safety indicators over time; Statistical tests were used to identify unusual safety events, and Kaplan-Meier survival curves were used to estimate the duration of the maintenance effect.

8. A path planning system for a highway maintenance vehicle, based on the path planning method for a highway maintenance vehicle according to any one of claims 1 to 7, characterized in that: Including data collection module, fusion module, path calculation module, prediction adjustment module, execution feedback module, analysis and optimization module; The data acquisition module is used to collect multimodal data of highway maintenance vehicles through sensors and 5G networks and upload them to the cloud data center for multimodal data fusion; The fusion module is used to quickly calculate the optimal path set based on the fused multimodal data using a quantum optimization algorithm; The path calculation module is used to calculate the optimal path, use the CADPOA algorithm to perform real-time path prediction and adjustment, and upload it to the cloud data center using the 5G network; The prediction and adjustment module is used for the highway maintenance vehicle to plan the driving route and perform maintenance tasks according to the optimal path instructions issued by the cloud data center; The execution feedback module is used for the road maintenance vehicles that are performing tasks on the road, and the cloud data center will continuously receive feedback data; After the analysis and optimization module is used to complete the task, the cloud data center will summarize the highway maintenance vehicle operation data and conduct in-depth analysis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the path planning method for a highway maintenance vehicle according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the path planning method for a highway maintenance vehicle according to any one of claims 1 to 7 are implemented.

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